Recipes

GeneformerRecipes

Bases: BaseModel

Pre-baked recipes for Geneformer.

THIS PYDANTIC MODEL IS NOT MEANT FOR SERIALIZATION. Only used to facilitate argparse. Each recipe should take args as the only argument. We use partials so we can provide this information at runtime. Add new recipes to this model.

Source code in bionemo/geneformer/run/recipes.py

class GeneformerRecipes(BaseModel):
    """Pre-baked recipes for Geneformer.

    THIS PYDANTIC MODEL IS NOT MEANT FOR SERIALIZATION. Only used to facilitate argparse. Each recipe should take `args`
    as the only argument. We use partials so we can provide this information at runtime. Add new recipes to this model.
    """

    # Use partials so we can still parameterize the recipes from the CLI (e.g. data paths.)
    geneformer_10m_finetune_recipe: Callable[
        [argparse.Namespace], MainConfig[ExposedFineTuneSeqLenBioBertConfig, GeneformerPretrainingDataConfig]
    ] = partial(geneformer_10m_finetune_recipe)
    geneformer_10m_pretrain_recipe: Callable[
        [argparse.Namespace], MainConfig[ExposedGeneformerPretrainConfig, GeneformerPretrainingDataConfig]
    ] = partial(geneformer_10m_pretrain_recipe)
    geneformer_10m_shortpretrain_recipe: Callable[
        [argparse.Namespace], MainConfig[ExposedGeneformerPretrainConfig, GeneformerPretrainingDataConfig]
    ] = partial(geneformer_10m_shortpretrain_recipe)
    geneformer_106m_pretrain_recipe: Callable[
        [argparse.Namespace], MainConfig[ExposedGeneformerPretrainConfig, GeneformerPretrainingDataConfig]
    ] = partial(geneformer_106m_pretrain_recipe)
    geneformer_tiny_test_recipe: Callable[
        [argparse.Namespace], MainConfig[ExposedGeneformerPretrainConfig, GeneformerPretrainingDataConfig]
    ] = partial(pretrain_tiny_test_recipe)
    finetune_test_recipe: Callable[
        [argparse.Namespace], MainConfig[ExposedFineTuneSeqLenBioBertConfig, GeneformerPretrainingDataConfig]
    ] = partial(finetune_test_recipe)

default_adam_optimizer_with_cosine_annealing_recipe()

Default optimizer scheduler config for Geneformer. See OptimizerSchedulerConfig for defaults.

Source code in bionemo/geneformer/run/recipes.py

def default_adam_optimizer_with_cosine_annealing_recipe() -> OptimizerSchedulerConfig:
    """Default optimizer scheduler config for Geneformer. See OptimizerSchedulerConfig for defaults."""
    return OptimizerSchedulerConfig()

default_trainer_config_recipe()

Default trainer config for Geneformer.

Source code in bionemo/geneformer/run/recipes.py

def default_trainer_config_recipe() -> TrainingConfig:
    """Default trainer config for Geneformer."""
    return TrainingConfig(max_steps=55000, limit_val_batches=2, val_check_interval=100)

experiment_config_recipe()

Default experiment config for Geneformer. Used in testing.

Source code in bionemo/geneformer/run/recipes.py

def experiment_config_recipe() -> ExperimentConfig:
    """Default experiment config for Geneformer. Used in testing."""
    return ExperimentConfig(
        save_every_n_steps=100,
        result_dir="./results",
        experiment_name="default_experiment",
        restore_from_checkpoint_path=None,
        save_last_checkpoint=True,
        metric_to_monitor_for_checkpoints="reduced_train_loss",
        save_top_k=2,
        create_tensorboard_logger=False,
    )

finetune_test_recipe(args)

Recipe for finetuning a regression head on the masked tokens.

Source code in bionemo/geneformer/run/recipes.py

def finetune_test_recipe(args) -> MainConfig[ExposedFineTuneSeqLenBioBertConfig, GeneformerPretrainingDataConfig]:
    """Recipe for finetuning a regression head on the masked tokens."""
    data_path = args.data_path
    result_dir = args.result_dir

    parallel_config = ParallelConfig(
        tensor_model_parallel_size=1, pipeline_model_parallel_size=1, num_devices=1, accumulate_grad_batches=2
    )
    training_config = TrainingConfig(
        max_steps=10, limit_val_batches=2, val_check_interval=2, precision="bf16-mixed", accelerator="gpu"
    )
    data_config = GeneformerPretrainingDataConfig(
        seq_length=128,
        micro_batch_size=2,
        num_dataset_workers=0,
        data_dir=data_path,
    )
    experiment_config = ExperimentConfig(
        save_every_n_steps=training_config.val_check_interval,
        result_dir=result_dir,
        experiment_name="test-experiment",
        restore_from_checkpoint_path=None,
        save_last_checkpoint=True,
        metric_to_monitor_for_checkpoints="reduced_train_loss",
        save_top_k=2,
        create_tensorboard_logger=False,
    )

    optim_config = OptimizerSchedulerConfig(lr_scheduler="cosine")
    geneformer_config = geneformer_10m_finetune_config(
        seq_length=data_config.seq_length, initial_ckpt_path=args.initial_ckpt_path
    )

    return MainConfig(
        data_config=data_config,
        parallel_config=parallel_config,
        training_config=training_config,
        bionemo_model_config=geneformer_config,
        optim_config=optim_config,
        experiment_config=experiment_config,
    )

geneformer_106m_experiment_config(result_dir)

Experiment config for Geneformer 106m.

Source code in bionemo/geneformer/run/recipes.py

def geneformer_106m_experiment_config(result_dir) -> ExperimentConfig:
    """Experiment config for Geneformer 106m."""
    return ExperimentConfig(
        save_every_n_steps=100,
        result_dir=result_dir,
        experiment_name="geneformer-106m",
        restore_from_checkpoint_path=None,
    )

geneformer_106m_model_config(seq_length=2048, precision='bf16-mixed', nemo1_init_path=None, initial_ckpt_path=None, biobert_spec_option=BiobertSpecOption.bert_layer_with_transformer_engine_spec)

Geneformer 106m model config settings.

Source code in bionemo/geneformer/run/recipes.py

def geneformer_106m_model_config(
    seq_length: int = 2048,
    precision: PrecisionTypes = "bf16-mixed",
    nemo1_init_path: Optional[str] = None,
    initial_ckpt_path: Optional[str] = None,
    biobert_spec_option: BiobertSpecOption = BiobertSpecOption.bert_layer_with_transformer_engine_spec,
) -> ExposedGeneformerPretrainConfig:
    """Geneformer 106m model config settings."""
    geneformer_config = ExposedGeneformerPretrainConfig(
        num_layers=12,
        hidden_size=768,
        ffn_hidden_size=3072,
        num_attention_heads=12,
        seq_length=seq_length,
        fp32_residual_connection=False,
        hidden_dropout=0.02,
        init_method_std=0.02,
        kv_channels=None,
        apply_query_key_layer_scaling=False,
        make_vocab_size_divisible_by=128,
        masked_softmax_fusion=True,
        fp16_lm_cross_entropy=False,
        params_dtype=precision,
        pipeline_dtype=precision,
        autocast_dtype=precision,
        gradient_accumulation_fusion=False,
        layernorm_zero_centered_gamma=False,
        layernorm_epsilon=1.0e-12,
        activation_func="gelu",
        qk_layernorm=False,
        apply_residual_connection_post_layernorm=False,
        bias_activation_fusion=True,
        bias_dropout_fusion=True,
        get_attention_mask_from_fusion=True,
        attention_dropout=0.1,
        share_embeddings_and_output_weights=True,
        enable_autocast=False,
        biobert_spec_option=biobert_spec_option,
        nemo1_ckpt_path=nemo1_init_path,
        initial_ckpt_path=initial_ckpt_path,
    )
    return geneformer_config

geneformer_106m_parallel_config()

Base parallel config for Geneformer.

Source code in bionemo/geneformer/run/recipes.py

def geneformer_106m_parallel_config() -> ParallelConfig:
    """Base parallel config for Geneformer."""
    return ParallelConfig(
        tensor_model_parallel_size=1,
        pipeline_model_parallel_size=1,
        accumulate_grad_batches=1,
        ddp="megatron",
        num_devices=8,
        num_nodes=1,
    )

geneformer_106m_pretrain_recipe(args)

Recipe for pretraining the 106m model. Uses 8 GPUs for data parallelism.

Source code in bionemo/geneformer/run/recipes.py

def geneformer_106m_pretrain_recipe(
    args,
) -> MainConfig[ExposedGeneformerPretrainConfig, GeneformerPretrainingDataConfig]:
    """Recipe for pretraining the 106m model. Uses 8 GPUs for data parallelism."""
    data_config: GeneformerPretrainingDataConfig = geneformer_data_recipe(data_dir=args.data_path)
    parallel_config = geneformer_106m_parallel_config()
    training_config = geneformer_base_training_config()
    bionemo_model_config = geneformer_106m_model_config(initial_ckpt_path=args.initial_ckpt_path)
    optim_config = geneformer_base_optimizer_scheduler_config()
    experiment_config = geneformer_106m_experiment_config(result_dir=args.result_dir)
    wandb_config = geneformer_106m_wandb_config()
    main_config = MainConfig[ExposedGeneformerPretrainConfig, GeneformerPretrainingDataConfig](
        data_config=data_config,
        parallel_config=parallel_config,
        training_config=training_config,
        bionemo_model_config=bionemo_model_config,
        optim_config=optim_config,
        experiment_config=experiment_config,
        wandb_config=wandb_config,
    )
    return main_config

geneformer_106m_wandb_config()

Wandb config for Geneformer 106m.

Source code in bionemo/geneformer/run/recipes.py

def geneformer_106m_wandb_config() -> WandbConfig:
    """Wandb config for Geneformer 106m."""
    wandb_config = WandbConfig(
        entity="geneformer-106m_pretraining",
        project="geneformer-106m_pretraining",
        group="geneformer-106m",
        tags=["geneformer-106m"],
        offline=True,
        anonymous=True,
        id="1",
        log_model=False,
    )
    return wandb_config

geneformer_10m_experiment_config(result_dir)

Experiment config for Geneformer 10m.

Source code in bionemo/geneformer/run/recipes.py

def geneformer_10m_experiment_config(result_dir) -> ExperimentConfig:
    """Experiment config for Geneformer 10m."""
    return ExperimentConfig(
        save_every_n_steps=100,
        result_dir=result_dir,
        experiment_name="geneformer-10m",
        restore_from_checkpoint_path=None,
    )

geneformer_10m_finetune_config(seq_length=2048, precision='bf16-mixed', nemo1_init_path=None, initial_ckpt_path=None, biobert_spec_option=BiobertSpecOption.bert_layer_with_transformer_engine_spec)

Geneformer 10m finetuning config settings.

Source code in bionemo/geneformer/run/recipes.py

def geneformer_10m_finetune_config(
    seq_length: int = 2048,
    precision: PrecisionTypes = "bf16-mixed",
    nemo1_init_path: Optional[str] = None,
    initial_ckpt_path: Optional[str] = None,
    biobert_spec_option=BiobertSpecOption.bert_layer_with_transformer_engine_spec,
) -> ExposedFineTuneSeqLenBioBertConfig:
    """Geneformer 10m finetuning config settings."""
    geneformer_config = ExposedFineTuneSeqLenBioBertConfig(
        num_layers=6,
        hidden_size=256,
        ffn_hidden_size=512,
        num_attention_heads=4,
        seq_length=seq_length,
        fp32_residual_connection=False,
        hidden_dropout=0.02,
        init_method_std=0.02,
        kv_channels=None,
        apply_query_key_layer_scaling=False,
        make_vocab_size_divisible_by=128,
        masked_softmax_fusion=True,
        fp16_lm_cross_entropy=False,
        params_dtype=precision,
        pipeline_dtype=precision,
        autocast_dtype=precision,
        gradient_accumulation_fusion=False,
        layernorm_zero_centered_gamma=False,
        layernorm_epsilon=1.0e-12,
        activation_func="gelu",
        qk_layernorm=False,
        apply_residual_connection_post_layernorm=False,
        bias_activation_fusion=True,
        bias_dropout_fusion=True,
        get_attention_mask_from_fusion=True,
        attention_dropout=0.1,
        share_embeddings_and_output_weights=True,
        enable_autocast=False,
        biobert_spec_option=biobert_spec_option,
        nemo1_ckpt_path=nemo1_init_path,
        initial_ckpt_path=initial_ckpt_path,
    )
    return geneformer_config

geneformer_10m_finetune_recipe(args)

Recipe for finetuning the 10m model on a token regression head. Used as an example and for testing.

Source code in bionemo/geneformer/run/recipes.py

def geneformer_10m_finetune_recipe(
    args,
) -> MainConfig[ExposedFineTuneSeqLenBioBertConfig, GeneformerPretrainingDataConfig]:
    """Recipe for finetuning the 10m model on a token regression head. Used as an example and for testing."""
    data_config: GeneformerPretrainingDataConfig = geneformer_data_recipe(data_dir=args.data_path)
    parallel_config = simple_parallel_recipe()
    training_config = default_trainer_config_recipe()
    bionemo_model_config = geneformer_finetuning_regression_head_recipe(initial_ckpt_path=args.initial_ckpt_path)
    optim_config = default_adam_optimizer_with_cosine_annealing_recipe()
    experiment_config = experiment_config_recipe()
    wandb_config = WandbConfig(
        project="bionemo2-demo",
        entity="nvidia",
        offline=True,
        tags=[],
        group="dev",
        id="dev",
        log_model=False,
        anonymous=True,
    )
    main_config = MainConfig[ExposedFineTuneSeqLenBioBertConfig, GeneformerPretrainingDataConfig](
        data_config=data_config,
        parallel_config=parallel_config,
        training_config=training_config,
        bionemo_model_config=bionemo_model_config,
        optim_config=optim_config,
        experiment_config=experiment_config,
        wandb_config=wandb_config,
    )
    return main_config

geneformer_10m_model_config(seq_length=2048, precision='bf16-mixed', nemo1_init_path=None, initial_ckpt_path=None, biobert_spec_option=BiobertSpecOption.bert_layer_with_transformer_engine_spec)

Geneformer 10m model config settings.

Source code in bionemo/geneformer/run/recipes.py

def geneformer_10m_model_config(
    seq_length: int = 2048,
    precision: PrecisionTypes = "bf16-mixed",
    nemo1_init_path: Optional[str] = None,
    initial_ckpt_path: Optional[str] = None,
    biobert_spec_option: BiobertSpecOption = BiobertSpecOption.bert_layer_with_transformer_engine_spec,
) -> ExposedGeneformerPretrainConfig:
    """Geneformer 10m model config settings."""
    geneformer_config = ExposedGeneformerPretrainConfig(
        num_layers=6,
        hidden_size=256,
        ffn_hidden_size=512,
        num_attention_heads=4,
        seq_length=seq_length,
        fp32_residual_connection=False,
        hidden_dropout=0.02,
        init_method_std=0.02,
        kv_channels=None,
        apply_query_key_layer_scaling=False,
        make_vocab_size_divisible_by=128,
        masked_softmax_fusion=True,
        fp16_lm_cross_entropy=False,
        params_dtype=precision,
        pipeline_dtype=precision,
        autocast_dtype=precision,
        gradient_accumulation_fusion=False,
        layernorm_zero_centered_gamma=False,
        layernorm_epsilon=1.0e-12,
        activation_func="gelu",
        qk_layernorm=False,
        apply_residual_connection_post_layernorm=False,
        bias_activation_fusion=True,
        bias_dropout_fusion=True,
        get_attention_mask_from_fusion=True,
        attention_dropout=0.1,
        share_embeddings_and_output_weights=True,
        enable_autocast=False,
        biobert_spec_option=biobert_spec_option,
        nemo1_ckpt_path=nemo1_init_path,
        initial_ckpt_path=initial_ckpt_path,
    )
    return geneformer_config

geneformer_10m_pretrain_recipe(args)

Recipe for pretraining the 10m model.

Source code in bionemo/geneformer/run/recipes.py

def geneformer_10m_pretrain_recipe(
    args,
) -> MainConfig[ExposedGeneformerPretrainConfig, GeneformerPretrainingDataConfig]:
    """Recipe for pretraining the 10m model."""
    data_config: GeneformerPretrainingDataConfig = geneformer_data_recipe(data_dir=args.data_path)
    parallel_config = simple_parallel_recipe()
    training_config = geneformer_base_training_config()
    bionemo_model_config = geneformer_10m_model_config(initial_ckpt_path=args.initial_ckpt_path)
    optim_config = geneformer_base_optimizer_scheduler_config()
    experiment_config = geneformer_10m_experiment_config(result_dir=args.result_dir)
    wandb_config = geneformer_10m_wandb_config()
    main_config = MainConfig[ExposedGeneformerPretrainConfig, GeneformerPretrainingDataConfig](
        data_config=data_config,
        parallel_config=parallel_config,
        training_config=training_config,
        bionemo_model_config=bionemo_model_config,
        optim_config=optim_config,
        experiment_config=experiment_config,
        wandb_config=wandb_config,
    )
    return main_config

geneformer_10m_shortpretrain_recipe(args)

Recipe for pretraining the 10m model.

Source code in bionemo/geneformer/run/recipes.py

def geneformer_10m_shortpretrain_recipe(
    args,
) -> MainConfig[ExposedGeneformerPretrainConfig, GeneformerPretrainingDataConfig]:
    """Recipe for pretraining the 10m model."""
    data_config: GeneformerPretrainingDataConfig = geneformer_data_recipe(data_dir=args.data_path)
    parallel_config = simple_parallel_recipe()
    training_config = geneformer_short_base_training_config()
    bionemo_model_config = geneformer_10m_model_config(initial_ckpt_path=args.initial_ckpt_path)
    optim_config = geneformer_base_optimizer_scheduler_config()
    experiment_config = geneformer_10m_experiment_config(result_dir=args.result_dir)
    wandb_config = geneformer_10m_wandb_config()
    main_config = MainConfig[ExposedGeneformerPretrainConfig, GeneformerPretrainingDataConfig](
        data_config=data_config,
        parallel_config=parallel_config,
        training_config=training_config,
        bionemo_model_config=bionemo_model_config,
        optim_config=optim_config,
        experiment_config=experiment_config,
        wandb_config=wandb_config,
    )
    return main_config

geneformer_10m_wandb_config()

Wandb config for Geneformer 10m.

Source code in bionemo/geneformer/run/recipes.py

def geneformer_10m_wandb_config() -> WandbConfig:
    """Wandb config for Geneformer 10m."""
    wandb_config = WandbConfig(
        entity="geneformer-10m_pretraining",
        project="geneformer-10m_pretraining",
        group="geneformer-10m",
        tags=["geneformer-10m"],
        offline=True,
        anonymous=True,
        id="1",
        log_model=False,
    )
    return wandb_config

geneformer_base_optimizer_scheduler_config()

Base optimizer scheduler config for Geneformer.

Source code in bionemo/geneformer/run/recipes.py

def geneformer_base_optimizer_scheduler_config() -> OptimizerSchedulerConfig:
    """Base optimizer scheduler config for Geneformer."""
    return OptimizerSchedulerConfig(lr=1e-3, lr_scheduler="cosine")  # Matches bionemo1

geneformer_base_parallel_config()

Base parallel config for Geneformer.

Source code in bionemo/geneformer/run/recipes.py

def geneformer_base_parallel_config() -> ParallelConfig:
    """Base parallel config for Geneformer."""
    return ParallelConfig(
        tensor_model_parallel_size=1,
        pipeline_model_parallel_size=1,
        accumulate_grad_batches=1,
        ddp="megatron",
        num_devices=1,
        num_nodes=1,
    )

geneformer_base_training_config()

Base training config for Geneformer.

Source code in bionemo/geneformer/run/recipes.py

def geneformer_base_training_config() -> TrainingConfig:
    """Base training config for Geneformer."""
    return TrainingConfig(
        max_steps=400000, limit_val_batches=8, val_check_interval=100, precision="bf16-mixed"
    )  # matches bionemo1

geneformer_data_recipe(data_dir)

Recipe that produces the base geneformer small data configuration.

Source code in bionemo/geneformer/run/recipes.py

def geneformer_data_recipe(data_dir) -> GeneformerPretrainingDataConfig:
    """Recipe that produces the base geneformer small data configuration."""
    return GeneformerPretrainingDataConfig(data_dir=data_dir)

geneformer_finetuning_regression_head_recipe(precision='bf16-mixed', nemo1_init_path=None, initial_ckpt_path=None, initial_ckpt_skip_keys_with_these_prefixes=None)

Recipe for finetuning a regression head on the masked tokens.

Source code in bionemo/geneformer/run/recipes.py

def geneformer_finetuning_regression_head_recipe(
    precision: PrecisionTypes = "bf16-mixed",
    nemo1_init_path: Optional[str] = None,
    initial_ckpt_path: Optional[str] = None,
    initial_ckpt_skip_keys_with_these_prefixes: Optional[List[str]] = None,
) -> ExposedFineTuneSeqLenBioBertConfig:
    """Recipe for finetuning a regression head on the masked tokens."""
    partial_finetuning_config = partial(
        ExposedFineTuneSeqLenBioBertConfig,
        params_dtype=precision,
        pipeline_dtype=precision,
        autocast_dtype=precision,
        nemo1_ckpt_path=nemo1_init_path,
        initial_ckpt_path=initial_ckpt_path,
        biobert_spec_option=BiobertSpecOption.bert_layer_with_transformer_engine_spec,
    )
    if initial_ckpt_skip_keys_with_these_prefixes:
        finetuning_config = partial_finetuning_config(
            initial_ckpt_skip_keys_with_these_prefixes=initial_ckpt_skip_keys_with_these_prefixes
        )
    else:
        # Use the sensible default when None is passed
        finetuning_config = partial_finetuning_config()
    return finetuning_config

geneformer_short_base_training_config()

Base training config for Geneformer.

Source code in bionemo/geneformer/run/recipes.py

def geneformer_short_base_training_config() -> TrainingConfig:
    """Base training config for Geneformer."""
    return TrainingConfig(
        max_steps=500, limit_val_batches=8, val_check_interval=100, precision="bf16-mixed"
    )  # matches bionemo1

geneformer_tiny_config(seq_length=2048, precision='bf16-mixed', nemo1_init_path=None, initial_ckpt_path=None, biobert_spec_option=BiobertSpecOption.bert_layer_with_transformer_engine_spec)

Geneformer tiny model config settings, used in testing.

Source code in bionemo/geneformer/run/recipes.py

def geneformer_tiny_config(
    seq_length: int = 2048,
    precision: PrecisionTypes = "bf16-mixed",
    nemo1_init_path: Optional[str] = None,
    initial_ckpt_path: Optional[str] = None,
    biobert_spec_option: BiobertSpecOption = BiobertSpecOption.bert_layer_with_transformer_engine_spec,
) -> ExposedGeneformerPretrainConfig:
    """Geneformer tiny model config settings, used in testing."""
    geneformer_config = ExposedGeneformerPretrainConfig(
        num_layers=2,
        hidden_size=32,
        ffn_hidden_size=4 * 32,
        num_attention_heads=2,
        seq_length=seq_length,
        fp32_residual_connection=False,
        hidden_dropout=0.02,
        init_method_std=0.02,
        kv_channels=None,
        apply_query_key_layer_scaling=False,
        make_vocab_size_divisible_by=128,
        masked_softmax_fusion=True,
        fp16_lm_cross_entropy=False,
        params_dtype=precision,
        pipeline_dtype=precision,
        autocast_dtype=precision,
        gradient_accumulation_fusion=False,
        layernorm_zero_centered_gamma=False,
        layernorm_epsilon=1.0e-12,
        activation_func="gelu",
        qk_layernorm=False,
        apply_residual_connection_post_layernorm=False,
        bias_activation_fusion=True,
        bias_dropout_fusion=True,
        get_attention_mask_from_fusion=True,
        attention_dropout=0.1,
        share_embeddings_and_output_weights=True,
        enable_autocast=False,
        biobert_spec_option=biobert_spec_option,
        nemo1_ckpt_path=nemo1_init_path,
        initial_ckpt_path=initial_ckpt_path,
    )
    return geneformer_config

pretrain_tiny_test_recipe(args)

Recipe for pretraining a tiny model. Used in testing.

Source code in bionemo/geneformer/run/recipes.py

def pretrain_tiny_test_recipe(args) -> MainConfig[ExposedGeneformerPretrainConfig, GeneformerPretrainingDataConfig]:
    """Recipe for pretraining a tiny model. Used in testing."""
    data_path = args.data_path
    result_dir = args.result_dir

    parallel_config = ParallelConfig(
        tensor_model_parallel_size=1, pipeline_model_parallel_size=1, num_devices=1, accumulate_grad_batches=2
    )
    training_config = TrainingConfig(
        max_steps=10, limit_val_batches=2, val_check_interval=2, precision="bf16-mixed", accelerator="gpu"
    )
    data_config = GeneformerPretrainingDataConfig(
        seq_length=128,
        micro_batch_size=2,
        num_dataset_workers=0,
        data_dir=data_path,
    )
    experiment_config = ExperimentConfig(
        save_every_n_steps=training_config.val_check_interval,
        result_dir=result_dir,
        experiment_name="test-experiment",
        restore_from_checkpoint_path=None,
        save_last_checkpoint=True,
        metric_to_monitor_for_checkpoints="reduced_train_loss",
        save_top_k=2,
        create_tensorboard_logger=False,
    )

    optim_config = OptimizerSchedulerConfig(lr_scheduler="cosine")
    geneformer_config = geneformer_tiny_config(
        seq_length=data_config.seq_length, initial_ckpt_path=args.initial_ckpt_path
    )

    return MainConfig(
        data_config=data_config,
        parallel_config=parallel_config,
        training_config=training_config,
        bionemo_model_config=geneformer_config,
        optim_config=optim_config,
        experiment_config=experiment_config,
    )

simple_parallel_recipe(tensor_model_parallel_size=1, pipeline_model_parallel_size=1, num_devices=1, accumulate_grad_batches=1)

Simple parallel config for Geneformer, only used in testing.

Source code in bionemo/geneformer/run/recipes.py

def simple_parallel_recipe(
    tensor_model_parallel_size: int = 1,
    pipeline_model_parallel_size: int = 1,
    num_devices: int = 1,
    accumulate_grad_batches: int = 1,
) -> ParallelConfig:
    """Simple parallel config for Geneformer, only used in testing."""
    assert num_devices >= tensor_model_parallel_size * pipeline_model_parallel_size, (
        "devices must be divisible by tensor_model_parallel_size * pipeline_model_parallel_size"
    )
    return ParallelConfig(
        tensor_model_parallel_size=tensor_model_parallel_size,
        pipeline_model_parallel_size=pipeline_model_parallel_size,
        accumulate_grad_batches=accumulate_grad_batches,
        num_devices=num_devices,
    )